Cast AI - EKS fully automated cost optimization and monitoring

What do you like best about the product?

I really like the user experience in CAST AI, especially the easy-to-use console. It allows us to manage clusters easily, see which policies are attached to which workload, and enable or disable workloads. This usability is one of the best parts for me.

What do you dislike about the product?

No such things

What problems is the product solving and how is that benefiting you?

I find CAST AI optimizes our infrastructure, reducing our monthly cloud costs from $32,000 to $20,000 while considering security with GPU metrics.

What do you like best about the product?

Cast AI is an outstanding Kubernetes cost optimization platform that has genuinely transformed how we manage our cloud infrastructure. The automated cost optimization is incredibly effective, reducing our cloud spend by over 50% without any manual effort. The AI-driven right-sizing of workloads is spot on, and the autopilot feature handles scaling seamlessly. The UI is intuitive and clean, making it easy to navigate and understand resource usage at a glance. Integration with our existing cloud providers (AWS, GCP, Azure) was smooth and took only minutes. The real-time cost visibility and recommendations are actionable and easy to implement. The support team is world-class and always responsive.

What do you dislike about the product?

Honestly, it is very hard to find anything to dislike about Cast AI. The product is so comprehensive that there is very little room for improvement. If I had to nitpick, I would say that the initial setup documentation could have a few more visual guides, but the support team more than compensates for this. Everything else — from onboarding to daily use — has been a pleasure. The platform keeps getting better with every update, and the team is clearly listening to user feedback and continuously improving the product.

What problems is the product solving and how is that benefiting you?

Before Cast AI, we struggled with unpredictable cloud costs and over-provisioned Kubernetes clusters that were wasting significant resources. Cast AI solved this completely. It automatically right-sizes our nodes, eliminates wasted capacity, and has reduced our monthly cloud bill by more than 50%. We no longer need to manually tune resource requests and limits — Cast AI handles it all intelligently. The ROI has been remarkable: within the first month, we recouped the cost of the subscription many times over. Our engineering team now spends less time on infrastructure optimization and more time building features, which has accelerated our product development considerably.

What do you like best about the product?

I use CAST AI for cost optimization, cost monitoring, and checking anomalies. The main thing I appreciate about CAST AI is its visibility in a common dashboard for cost monitoring and CPU and memory usage per pod. I love the workload autoscaler because it provides the right sizing of pods. It learns from the usage pattern over the last seven days of data, which helps us save resources. The autoscaler automatically rightsizes the pods based on the resource and limits provided, eliminating the need for manual tasks. It also manages the Replica count, HPA, and VPA intelligently. The classic console provides much ease of use. Setting up CAST AI was very easy, and with the mentioned steps, a cluster can be onboarded in no time.

What do you dislike about the product?

Sometimes the cluster has to be reconciled to enable rebalancing. While it connects efficiently to AWS, Azure, and GCP, the integration with Oracle needs to be added.

What problems is the product solving and how is that benefiting you?

I use CAST AI for cost optimization and monitoring, providing visibility in a common dashboard. It saves costs via workload autoscaling by right-sizing pods based on usage patterns, which eliminates manual tasks like managing replicas, HPA, and VPA.

What do you like best about the product?

I use CAST AI to optimize Kubernetes infrastructure cost and resource utilization across both production and non-production environments. CAST AI helps us automate node scaling, workload right-sizing, and instance type selection, which is a huge help. I also appreciate the dashboard view, which makes it easy to see all namespace workloads and identify usage patterns to reduce resources. CAST AI effectively solves challenges related to Kubernetes cost management, scalability, and operations across multiple cloud provider clusters. The cost management is fantastic, as CAST AI chooses the best instance types based on workloads and automatically provisions the right size of nodes, CPUs, and memory, which saves costs. The initial setup was quite good, and it helped us learn more about cost optimization. Overall, I would definitely recommend CAST AI.

What do you dislike about the product?

I would say CAST AI can improve by automatically picking the pod usage and changing the resources without any downtime of production workloads. Basically, right sizing is shown in the dashboard but we have to do it manually. It would be better if it could solve this automatically to spin up new pods and reduce the workloads.

What problems is the product solving and how is that benefiting you?

I use CAST AI to optimize Kubernetes infrastructure costs and resource utilization. It automates node scaling, workload right-sizing, and selects cost-effective instance types, helping us manage multiple cloud provider clusters efficiently.

What do you like best about the product?

What I value most is the granular, real-time visibility and the "app-aware" engine that scales resources based on actual workload DNA rather than just generic metrics. The seamless integration with our existing CI/CD pipelines meant we saw performance improvements and massive cost reductions within hours of deployment. It has effectively bridged the gap between our DevOps and FinOps goals through one unified, automated control plane

What do you dislike about the product?

While the automation is powerful, the "black box" nature of the decision logic can initially make it difficult to trust the system with mission-critical production workloads without extensive testing of the guardrails. We also found that the coordination between the Workload Autoscaler and Node Autoscaler could be tighter, as they sometimes operate independently rather than planning for future node utilization in perfect tandem. Additionally, the "percentage of savings" pricing model can feel like a "savings tax" as you scale, making it harder to predict long-term tool costs compared to a flat-tier subscription

What problems is the product solving and how is that benefiting you?

Cast AI solves the persistent "Kubernetes waste" problem by automating rightsizing, bin-packing, and spot instance orchestration that are traditionally too complex to manage manually at scale. For me, this has replaced hours of tedious YAML tuning and "firefighting" during traffic spikes with a reliable, autonomous engine that keeps our clusters lean and high-performing. The biggest benefit is the reclaimed time; I can finally focus on high-impact architectural work instead of constantly babysitting node groups and cloud bills.